ML lifecycle phase - Business goal - Machine Learning Lens

ML lifecycle phase - Business goal

Business goal identification is the most important phase of the ML lifecycle. An organization considering ML should have a clear idea of the problem to be solved, and the business value to be gained. You must be able to measure business value against specific business objectives and success criteria. While this holds true for any technical solution, this step is particularly challenging when considering ML solutions because ML is a constantly evolving technology. 

After you determine your criteria for success, evaluate your organization's ability to move toward that target. The target should be achievable and provide a clear path to production. Involve all relevant stakeholders from the beginning to align them to this target and any new business processes that result from this initiative.

Start the review by determining if ML is the appropriate approach for delivering your business goal. Evaluate all of the options that you have available for achieving the goal. Determine how accurate the resulting outcomes would be, while considering the cost and scalability of each approach.

For an ML-based approach to be successful, ensure that enough relevant, high-quality training data is available to the algorithm. Carefully evaluate the data to make sure that the correct data sources are available and accessible.

Steps in this phase:

The following work steps should be followed to establish your business goals.

  • Business considerations

    • Understand business requirements.

    • Align affected stakeholders with this initiative.

    • Form a business question.

    • Identify critical, must-have features.

    • Consider new business processes that might come out of this implementation.

    • Consider how business value can be measured using business metrics that the ML model can help to improve.

  • Frame the ML problem

    • Define the machine learning task based on the business question.

    • Review proven or published works in similar domains, if available.

    • Design small, focused POCs to validate those aspects of the approach where inadequate confidence exists.

  • Determine the optimization objective

    • Determine key business performance metrics for the ML use case, such as uplift in new business acquisition, fraud detection rate, and anomaly detection. Increase CSAT according to the business needs.

  • Review data requirements

    • Review the project’s ML feasibility and data requirements.

  • Cost and performance optimization

    • Evaluate the cost of data acquisition, training, inference, and wrong predictions.

    • Evaluate whether bringing in external data sources might improve model performance.

  • Production considerations

    • Review how to handle ML-generated errors.

    • Establish pathways to production.